Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations165034
Missing cells0
Missing cells (%)0.0%
Total size in memory17.6 MiB
Average record size in memory112.0 B

Variable types

Numeric11
Text3

Alerts

id is uniformly distributedUniform
id has unique valuesUnique
Tenure has 5007 (3.0%) zerosZeros
Balance has 89648 (54.3%) zerosZeros
HasCrCard has 40606 (24.6%) zerosZeros
IsActiveMember has 82885 (50.2%) zerosZeros
Exited has 130113 (78.8%) zerosZeros

Reproduction

Analysis started2024-11-01 19:18:57.538783
Analysis finished2024-11-01 19:21:01.264749
Duration2 minutes and 3.73 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct165034
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82516.5
Minimum0
Maximum165033
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:01.611098image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8251.65
Q141258.25
median82516.5
Q3123774.75
95-th percentile156781.35
Maximum165033
Range165033
Interquartile range (IQR)82516.5

Descriptive statistics

Standard deviation47641.3565
Coefficient of variation (CV)0.5773555168
Kurtosis-1.2
Mean82516.5
Median Absolute Deviation (MAD)41258.5
Skewness0
Sum1.361802806 × 1010
Variance2269698849
MonotonicityStrictly increasing
2024-11-01T20:21:02.050779image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
109995 1
 
< 0.1%
110017 1
 
< 0.1%
110018 1
 
< 0.1%
110019 1
 
< 0.1%
110020 1
 
< 0.1%
110021 1
 
< 0.1%
110022 1
 
< 0.1%
110023 1
 
< 0.1%
110024 1
 
< 0.1%
Other values (165024) 165024
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
ValueCountFrequency (%)
165033 1
< 0.1%
165032 1
< 0.1%
165031 1
< 0.1%
165030 1
< 0.1%
165029 1
< 0.1%

CustomerId
Real number (ℝ)

Distinct23221
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15692005.02
Minimum15565701
Maximum15815690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:02.558167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum15565701
5-th percentile15581248
Q115633141
median15690169
Q315756824
95-th percentile15800514
Maximum15815690
Range249989
Interquartile range (IQR)123683

Descriptive statistics

Standard deviation71397.81679
Coefficient of variation (CV)0.004549948633
Kurtosis-1.20742087
Mean15692005.02
Median Absolute Deviation (MAD)62240
Skewness-0.02293983332
Sum2.589714356 × 1012
Variance5097648242
MonotonicityNot monotonic
2024-11-01T20:21:03.035175image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15682355 121
 
0.1%
15570194 99
 
0.1%
15585835 98
 
0.1%
15595588 91
 
0.1%
15648067 90
 
0.1%
15793331 90
 
0.1%
15585067 86
 
0.1%
15782530 85
 
0.1%
15809837 84
 
0.1%
15806901 84
 
0.1%
Other values (23211) 164106
99.4%
ValueCountFrequency (%)
15565701 3
< 0.1%
15565706 4
< 0.1%
15565714 3
< 0.1%
15565759 1
 
< 0.1%
15565779 5
< 0.1%
ValueCountFrequency (%)
15815690 12
< 0.1%
15815670 1
 
< 0.1%
15815660 4
 
< 0.1%
15815656 2
 
< 0.1%
15815645 13
< 0.1%
Distinct2797
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:04.366677image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length23
Median length16
Mean length6.543178981
Min length2

Characters and Unicode

Total characters1079847
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique226 ?
Unique (%)0.1%

Sample

1st rowOkwudilichukwu
2nd rowOkwudiliolisa
3rd rowHsueh
4th rowKao
5th rowChiemenam
ValueCountFrequency (%)
hsia 2456
 
1.5%
t'ien 2282
 
1.4%
hs 1611
 
1.0%
kao 1577
 
0.9%
maclean 1577
 
0.9%
ts'ui 1567
 
0.9%
p'eng 1503
 
0.9%
h 1420
 
0.9%
hsueh 1306
 
0.8%
lo 1276
 
0.8%
Other values (2795) 149885
90.0%
2024-11-01T20:21:06.058352image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 101025
 
9.4%
e 95515
 
8.8%
i 88998
 
8.2%
n 85324
 
7.9%
o 66511
 
6.2%
u 60878
 
5.6%
h 46288
 
4.3%
s 40086
 
3.7%
r 35578
 
3.3%
l 32621
 
3.0%
Other values (45) 427023
39.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 885549
82.0%
Uppercase Letter 170025
 
15.7%
Other Punctuation 22756
 
2.1%
Space Separator 1426
 
0.1%
Dash Punctuation 91
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 101025
11.4%
e 95515
10.8%
i 88998
 
10.1%
n 85324
 
9.6%
o 66511
 
7.5%
u 60878
 
6.9%
h 46288
 
5.2%
s 40086
 
4.5%
r 35578
 
4.0%
l 32621
 
3.7%
Other values (16) 232725
26.3%
Uppercase Letter
ValueCountFrequency (%)
C 22587
13.3%
T 17837
 
10.5%
H 14904
 
8.8%
M 13987
 
8.2%
O 11631
 
6.8%
N 9912
 
5.8%
L 9506
 
5.6%
P 8186
 
4.8%
S 7116
 
4.2%
K 6379
 
3.8%
Other values (15) 47980
28.2%
Other Punctuation
ValueCountFrequency (%)
' 16426
72.2%
? 6330
 
27.8%
Space Separator
ValueCountFrequency (%)
1426
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 91
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1055574
97.8%
Common 24273
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 101025
 
9.6%
e 95515
 
9.0%
i 88998
 
8.4%
n 85324
 
8.1%
o 66511
 
6.3%
u 60878
 
5.8%
h 46288
 
4.4%
s 40086
 
3.8%
r 35578
 
3.4%
l 32621
 
3.1%
Other values (41) 402750
38.2%
Common
ValueCountFrequency (%)
' 16426
67.7%
? 6330
 
26.1%
1426
 
5.9%
- 91
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1079847
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 101025
 
9.4%
e 95515
 
8.8%
i 88998
 
8.2%
n 85324
 
7.9%
o 66511
 
6.2%
u 60878
 
5.6%
h 46288
 
4.3%
s 40086
 
3.7%
r 35578
 
3.3%
l 32621
 
3.0%
Other values (45) 427023
39.5%

CreditScore
Real number (ℝ)

Distinct457
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean656.454373
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:06.525745image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile524
Q1597
median659
Q3710
95-th percentile787
Maximum850
Range500
Interquartile range (IQR)113

Descriptive statistics

Standard deviation80.10334049
Coefficient of variation (CV)0.1220242317
Kurtosis-0.06745230234
Mean656.454373
Median Absolute Deviation (MAD)55
Skewness-0.05929121968
Sum108337291
Variance6416.545157
MonotonicityNot monotonic
2024-11-01T20:21:06.986184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 2532
 
1.5%
678 2299
 
1.4%
684 1718
 
1.0%
667 1658
 
1.0%
705 1605
 
1.0%
683 1574
 
1.0%
651 1496
 
0.9%
682 1470
 
0.9%
663 1464
 
0.9%
679 1439
 
0.9%
Other values (447) 147779
89.5%
ValueCountFrequency (%)
350 19
< 0.1%
358 1
 
< 0.1%
359 2
 
< 0.1%
363 4
 
< 0.1%
365 6
 
< 0.1%
ValueCountFrequency (%)
850 2532
1.5%
849 85
 
0.1%
848 28
 
< 0.1%
847 48
 
< 0.1%
846 44
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:08.133721image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.990262613
Min length5

Characters and Unicode

Total characters988597
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowFrance
3rd rowFrance
4th rowFrance
5th rowSpain
ValueCountFrequency (%)
france 94215
57.1%
spain 36213
 
21.9%
germany 34606
 
21.0%
2024-11-01T20:21:08.996364image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 165034
16.7%
n 165034
16.7%
r 128821
13.0%
e 128821
13.0%
F 94215
9.5%
c 94215
9.5%
S 36213
 
3.7%
p 36213
 
3.7%
i 36213
 
3.7%
G 34606
 
3.5%
Other values (2) 69212
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 823563
83.3%
Uppercase Letter 165034
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 165034
20.0%
n 165034
20.0%
r 128821
15.6%
e 128821
15.6%
c 94215
11.4%
p 36213
 
4.4%
i 36213
 
4.4%
m 34606
 
4.2%
y 34606
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
F 94215
57.1%
S 36213
 
21.9%
G 34606
 
21.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 988597
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 165034
16.7%
n 165034
16.7%
r 128821
13.0%
e 128821
13.0%
F 94215
9.5%
c 94215
9.5%
S 36213
 
3.7%
p 36213
 
3.7%
i 36213
 
3.7%
G 34606
 
3.5%
Other values (2) 69212
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 988597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 165034
16.7%
n 165034
16.7%
r 128821
13.0%
e 128821
13.0%
F 94215
9.5%
c 94215
9.5%
S 36213
 
3.7%
p 36213
 
3.7%
i 36213
 
3.7%
G 34606
 
3.5%
Other values (2) 69212
7.0%

Gender
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:09.447047image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.871141704
Min length4

Characters and Unicode

Total characters803904
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale
ValueCountFrequency (%)
male 93150
56.4%
female 71884
43.6%
2024-11-01T20:21:10.294214image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 236918
29.5%
a 165034
20.5%
l 165034
20.5%
M 93150
 
11.6%
F 71884
 
8.9%
m 71884
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 638870
79.5%
Uppercase Letter 165034
 
20.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 236918
37.1%
a 165034
25.8%
l 165034
25.8%
m 71884
 
11.3%
Uppercase Letter
ValueCountFrequency (%)
M 93150
56.4%
F 71884
43.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 803904
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 236918
29.5%
a 165034
20.5%
l 165034
20.5%
M 93150
 
11.6%
F 71884
 
8.9%
m 71884
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 803904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 236918
29.5%
a 165034
20.5%
l 165034
20.5%
M 93150
 
11.6%
F 71884
 
8.9%
m 71884
 
8.9%

Age
Real number (ℝ)

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.12588788
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:10.711533image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q132
median37
Q342
95-th percentile56
Maximum92
Range74
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.867204591
Coefficient of variation (CV)0.2325769991
Kurtosis1.532405754
Mean38.12588788
Median Absolute Deviation (MAD)5
Skewness0.9680627926
Sum6292067.78
Variance78.62731727
MonotonicityNot monotonic
2024-11-01T20:21:11.180167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 9255
 
5.6%
38 9246
 
5.6%
35 9118
 
5.5%
34 8625
 
5.2%
36 8556
 
5.2%
33 8367
 
5.1%
40 7996
 
4.8%
39 7944
 
4.8%
32 7712
 
4.7%
31 7093
 
4.3%
Other values (61) 81122
49.2%
ValueCountFrequency (%)
18 118
 
0.1%
19 214
 
0.1%
20 336
 
0.2%
21 593
0.4%
22 933
0.6%
ValueCountFrequency (%)
92 11
< 0.1%
85 3
 
< 0.1%
84 4
 
< 0.1%
83 3
 
< 0.1%
82 7
< 0.1%

Tenure
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.020353382
Minimum0
Maximum10
Zeros5007
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:11.619603image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.806158567
Coefficient of variation (CV)0.5589563828
Kurtosis-1.157921179
Mean5.020353382
Median Absolute Deviation (MAD)2
Skewness0.006489806236
Sum828529
Variance7.874525901
MonotonicityNot monotonic
2024-11-01T20:21:11.982742image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 18045
10.9%
7 17810
10.8%
4 17554
10.6%
8 17520
10.6%
5 17268
10.5%
1 16760
10.2%
9 16709
10.1%
3 16630
10.1%
6 15822
9.6%
10 5909
 
3.6%
ValueCountFrequency (%)
0 5007
 
3.0%
1 16760
10.2%
2 18045
10.9%
3 16630
10.1%
4 17554
10.6%
ValueCountFrequency (%)
10 5909
 
3.6%
9 16709
10.1%
8 17520
10.6%
7 17810
10.8%
6 15822
9.6%

Balance
Real number (ℝ)

ZEROS 

Distinct30075
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55478.08669
Minimum0
Maximum250898.09
Zeros89648
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:12.476228image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3119939.5175
95-th percentile151671.55
Maximum250898.09
Range250898.09
Interquartile range (IQR)119939.5175

Descriptive statistics

Standard deviation62817.66328
Coefficient of variation (CV)1.132296859
Kurtosis-1.600851856
Mean55478.08669
Median Absolute Deviation (MAD)0
Skewness0.3820204484
Sum9155770559
Variance3946058820
MonotonicityNot monotonic
2024-11-01T20:21:13.027664image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89648
54.3%
124577.33 88
 
0.1%
127864.4 64
 
< 0.1%
122314.5 63
 
< 0.1%
129855.32 59
 
< 0.1%
126473.33 56
 
< 0.1%
102773.2 52
 
< 0.1%
122453.97 48
 
< 0.1%
137936.94 42
 
< 0.1%
123544 41
 
< 0.1%
Other values (30065) 74873
45.4%
ValueCountFrequency (%)
0 89648
54.3%
18.33 1
 
< 0.1%
3768.69 3
 
< 0.1%
9053.36 1
 
< 0.1%
9904.42 1
 
< 0.1%
ValueCountFrequency (%)
250898.09 3
 
< 0.1%
238387.56 8
< 0.1%
222267.63 5
< 0.1%
221532.8 4
< 0.1%
216109.88 2
 
< 0.1%

NumOfProducts
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.554455446
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:13.470115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5471536788
Coefficient of variation (CV)0.3519905832
Kurtosis-0.2780591334
Mean1.554455446
Median Absolute Deviation (MAD)0
Skewness0.3682779795
Sum256538
Variance0.2993771483
MonotonicityNot monotonic
2024-11-01T20:21:13.835242image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2 84291
51.1%
1 77374
46.9%
3 2894
 
1.8%
4 475
 
0.3%
ValueCountFrequency (%)
1 77374
46.9%
2 84291
51.1%
3 2894
 
1.8%
4 475
 
0.3%
ValueCountFrequency (%)
4 475
 
0.3%
3 2894
 
1.8%
2 84291
51.1%
1 77374
46.9%

HasCrCard
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7539537307
Minimum0
Maximum1
Zeros40606
Zeros (%)24.6%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:14.266350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.430707124
Coefficient of variation (CV)0.5712646632
Kurtosis-0.6093645524
Mean0.7539537307
Median Absolute Deviation (MAD)0
Skewness-1.179255202
Sum124428
Variance0.1855086267
MonotonicityNot monotonic
2024-11-01T20:21:14.674321image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 124428
75.4%
0 40606
 
24.6%
ValueCountFrequency (%)
0 40606
 
24.6%
1 124428
75.4%
ValueCountFrequency (%)
1 124428
75.4%
0 40606
 
24.6%

IsActiveMember
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4977701565
Minimum0
Maximum1
Zeros82885
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:15.112918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4999965426
Coefficient of variation (CV)1.004472719
Kurtosis-1.999944679
Mean0.4977701565
Median Absolute Deviation (MAD)0
Skewness0.008919543959
Sum82149
Variance0.2499965426
MonotonicityNot monotonic
2024-11-01T20:21:15.530897image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 82885
50.2%
1 82149
49.8%
ValueCountFrequency (%)
0 82885
50.2%
1 82149
49.8%
ValueCountFrequency (%)
1 82149
49.8%
0 82885
50.2%

EstimatedSalary
Real number (ℝ)

Distinct55298
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112574.8227
Minimum11.58
Maximum199992.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:16.090187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum11.58
5-th percentile21198.39
Q174637.57
median117948
Q3155152.4675
95-th percentile183490.27
Maximum199992.48
Range199980.9
Interquartile range (IQR)80514.8975

Descriptive statistics

Standard deviation50292.86558
Coefficient of variation (CV)0.4467505643
Kurtosis-0.8388447807
Mean112574.8227
Median Absolute Deviation (MAD)40111.035
Skewness-0.3090215397
Sum1.85786733 × 1010
Variance2529372329
MonotonicityNot monotonic
2024-11-01T20:21:16.761200image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88890.05 178
 
0.1%
140941.47 107
 
0.1%
167984.72 100
 
0.1%
90876.95 98
 
0.1%
129964.94 98
 
0.1%
181224.56 95
 
0.1%
16081.62 93
 
0.1%
121151.1 90
 
0.1%
131736.23 89
 
0.1%
141872.05 89
 
0.1%
Other values (55288) 163997
99.4%
ValueCountFrequency (%)
11.58 10
< 0.1%
11.8 1
 
< 0.1%
90.07 2
 
< 0.1%
91.75 2
 
< 0.1%
96.27 1
 
< 0.1%
ValueCountFrequency (%)
199992.48 20
< 0.1%
199992.45 1
 
< 0.1%
199992.36 1
 
< 0.1%
199992.29 1
 
< 0.1%
199970.74 1
 
< 0.1%

Exited
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2115988221
Minimum0
Maximum1
Zeros130113
Zeros (%)78.8%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-11-01T20:21:17.364672image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4084431067
Coefficient of variation (CV)1.930271174
Kurtosis-0.005649802006
Mean0.2115988221
Median Absolute Deviation (MAD)0
Skewness1.412214667
Sum34921
Variance0.1668257714
MonotonicityNot monotonic
2024-11-01T20:21:17.827089image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 130113
78.8%
1 34921
 
21.2%
ValueCountFrequency (%)
0 130113
78.8%
1 34921
 
21.2%
ValueCountFrequency (%)
1 34921
 
21.2%
0 130113
78.8%

Interactions

2024-11-01T20:20:54.511859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:06.618295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:11.588151image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:16.438439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:21.086539image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:25.793522image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:30.351836image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:34.954859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:40.375809image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:45.005517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:49.603530image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:54.886231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:07.158805image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:12.037640image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:16.871529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:21.524406image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:26.209613image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:30.792473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:35.397370image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:40.790963image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:45.401559image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:50.045914image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:55.286932image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:07.607867image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:12.441575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:17.307032image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:21.943529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:26.628667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:31.203777image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:35.824008image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:41.206493image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:45.758338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:50.514357image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:55.689590image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:08.053039image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:12.843630image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:17.695966image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:22.357751image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:27.039375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:31.632153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:36.247253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:41.610520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:46.181534image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:50.947648image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:56.129335image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:08.539878image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:13.306504image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:18.145825image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:22.782406image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:27.465174image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:32.075287image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:37.346144image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:42.050903image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:46.586107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:51.382474image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:56.502242image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:08.966401image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:13.728478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:18.561805image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:23.201622image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:27.865612image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:32.481461image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:37.777395image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:42.486666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:46.989658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:51.809368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:56.906634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:09.401223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:14.168321image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:18.983964image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:23.612851image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:28.254889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:32.863529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:38.193937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:42.905983image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:47.416713image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:52.232895image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:57.356810image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:09.834178image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:14.609925image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:19.420500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:24.060823image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:28.665854image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:33.291061image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:38.633600image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:43.342795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:47.814447image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:52.722355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:57.778965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:10.279647image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:15.060034image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:19.834646image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:24.467355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:29.102165image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:33.685999image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:39.065764image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:43.755660image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:48.200624image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:53.168303image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:58.193150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:10.706956image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:15.501875image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:20.272744image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:24.892163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:29.515198image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:34.068698image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:39.493562image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:44.174070image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:48.712057image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:53.647013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:58.661357image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:11.169285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:15.987292image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:20.709130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:25.361787image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:29.938952image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:34.532212image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:39.963340image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:44.615785image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:49.192583image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-01T20:20:54.105483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2024-11-01T20:21:19.739897image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
AgeBalanceCreditScoreCustomerIdEstimatedSalaryExitedHasCrCardIsActiveMemberNumOfProductsTenureid
Age1.0000.066-0.0120.003-0.0010.355-0.012-0.030-0.129-0.0090.004
Balance0.0661.0000.006-0.0090.0070.126-0.017-0.015-0.382-0.0090.001
CreditScore-0.0120.0061.0000.008-0.001-0.029-0.0030.0140.0130.001-0.001
CustomerId0.003-0.0090.0081.0000.003-0.010-0.006-0.0030.006-0.001-0.000
EstimatedSalary-0.0010.007-0.0010.0031.0000.0200.004-0.008-0.0040.001-0.002
Exited0.3550.126-0.029-0.0100.0201.000-0.022-0.210-0.267-0.0190.003
HasCrCard-0.012-0.017-0.003-0.0060.004-0.0221.000-0.0210.0080.005-0.005
IsActiveMember-0.030-0.0150.014-0.003-0.008-0.210-0.0211.0000.050-0.0060.000
NumOfProducts-0.129-0.3820.0130.006-0.004-0.2670.0080.0501.0000.0080.000
Tenure-0.009-0.0090.001-0.0010.001-0.0190.005-0.0060.0081.000-0.003
id0.0040.001-0.001-0.000-0.0020.003-0.0050.0000.000-0.0031.000
2024-11-01T20:21:20.565084image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
idCustomerIdCreditScoreAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExited
id1.000-0.000-0.0010.004-0.0030.001-0.000-0.0050.000-0.0020.003
CustomerId-0.0001.0000.0070.003-0.001-0.0080.004-0.005-0.0030.003-0.010
CreditScore-0.0010.0071.000-0.0090.0010.0070.011-0.0030.015-0.002-0.027
Age0.0040.003-0.0091.000-0.0110.064-0.102-0.0120.003-0.0050.341
Tenure-0.003-0.0010.001-0.0111.000-0.0090.0070.005-0.0060.001-0.020
Balance0.001-0.0080.0070.064-0.0091.000-0.361-0.019-0.0150.0090.130
NumOfProducts-0.0000.0040.011-0.1020.007-0.3611.0000.0050.040-0.004-0.215
HasCrCard-0.005-0.005-0.003-0.0120.005-0.0190.0051.000-0.0210.004-0.022
IsActiveMember0.000-0.0030.0150.003-0.006-0.0150.040-0.0211.000-0.008-0.210
EstimatedSalary-0.0020.003-0.002-0.0050.0010.009-0.0040.004-0.0081.0000.019
Exited0.003-0.010-0.0270.341-0.0200.130-0.215-0.022-0.2100.0191.000
2024-11-01T20:21:21.343432image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
idCustomerIdCreditScoreAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExited
id1.000-0.000-0.0010.004-0.0030.0010.000-0.0050.000-0.0020.003
CustomerId-0.0001.0000.0080.003-0.001-0.0090.006-0.006-0.0030.003-0.010
CreditScore-0.0010.0081.000-0.0120.0010.0060.013-0.0030.014-0.001-0.029
Age0.0040.003-0.0121.000-0.0090.066-0.129-0.012-0.030-0.0010.355
Tenure-0.003-0.0010.001-0.0091.000-0.0090.0080.005-0.0060.001-0.019
Balance0.001-0.0090.0060.066-0.0091.000-0.382-0.017-0.0150.0070.126
NumOfProducts0.0000.0060.013-0.1290.008-0.3821.0000.0080.050-0.004-0.267
HasCrCard-0.005-0.006-0.003-0.0120.005-0.0170.0081.000-0.0210.004-0.022
IsActiveMember0.000-0.0030.014-0.030-0.006-0.0150.050-0.0211.000-0.008-0.210
EstimatedSalary-0.0020.003-0.001-0.0010.0010.007-0.0040.004-0.0081.0000.020
Exited0.003-0.010-0.0290.355-0.0190.126-0.267-0.022-0.2100.0201.000
2024-11-01T20:21:22.183029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
idCustomerIdCreditScoreAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExited
id1.000-0.000-0.0010.003-0.0020.0010.000-0.0040.000-0.0010.002
CustomerId-0.0001.0000.0050.002-0.001-0.0070.005-0.005-0.0020.002-0.008
CreditScore-0.0010.0051.000-0.0080.0010.0040.011-0.0020.012-0.001-0.024
Age0.0030.002-0.0081.000-0.0070.049-0.106-0.010-0.025-0.0000.295
Tenure-0.002-0.0010.001-0.0071.000-0.0070.0070.004-0.0050.001-0.017
Balance0.001-0.0070.0040.049-0.0071.000-0.337-0.015-0.0130.0050.113
NumOfProducts0.0000.0050.011-0.1060.007-0.3371.0000.0080.050-0.004-0.264
HasCrCard-0.004-0.005-0.002-0.0100.004-0.0150.0081.000-0.0210.003-0.022
IsActiveMember0.000-0.0020.012-0.025-0.005-0.0130.050-0.0211.000-0.006-0.210
EstimatedSalary-0.0010.002-0.001-0.0000.0010.005-0.0040.003-0.0061.0000.016
Exited0.002-0.008-0.0240.295-0.0170.113-0.264-0.022-0.2100.0161.000
2024-11-01T20:21:23.027588image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
idCustomerIdCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExited
id1.0000.0110.0080.0000.0020.0000.0000.0070.0000.0060.0000.0000.006
CustomerId0.0111.0000.0360.0110.0130.0340.0180.0410.0100.0210.0090.0440.018
CreditScore0.0080.0361.0000.0310.0080.0370.0260.0470.0260.0380.0170.0480.049
Geography0.0000.0110.0311.0000.0240.1430.0290.5590.1120.0100.0220.0350.128
Gender0.0020.0130.0080.0241.0000.1000.0110.0240.0860.0090.0530.0210.228
Age0.0000.0340.0370.1430.1001.0000.0320.0980.2000.0240.1380.0480.522
Tenure0.0000.0180.0260.0290.0110.0321.0000.0410.0410.0150.0210.0260.049
Balance0.0070.0410.0470.5590.0240.0980.0411.0000.3970.0360.0220.0570.189
NumOfProducts0.0000.0100.0260.1120.0860.2000.0410.3971.0000.0200.1270.0260.607
HasCrCard0.0060.0210.0380.0100.0090.0240.0150.0360.0201.0000.0330.0190.035
IsActiveMember0.0000.0090.0170.0220.0530.1380.0210.0220.1270.0331.0000.0200.324
EstimatedSalary0.0000.0440.0480.0350.0210.0480.0260.0570.0260.0190.0201.0000.034
Exited0.0060.0180.0490.1280.2280.5220.0490.1890.6070.0350.3240.0341.000

Missing values

2024-11-01T20:20:59.294201image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-01T20:21:00.428148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.